Plain-language interpretation of a result

vlm_interpret() turns a result of the package into a short report: a headline, findings, recommendations and cautions, computed from its numbers, with the sources of the method. It covers vlm_fit(), vlm_lsmeans(), vlm_estimate(), vlm_contrast(), vlm_blup(), vlm_ems() and vlm_compare().

Usage

vlm_interpret(x, alpha = 0.05, top = 3L, ...)

# Default S3 method
vlm_interpret(x, alpha = 0.05, top = 3L, ...)

# S3 method for class 'vlm_interpretation'
print(x, ...)

# S3 method for class 'vlm_interpretation'
format(x, style = c("text", "markdown"), width = getOption("width", 80L), ...)

Arguments

x: a result of a function of the package.
alpha: significance level used in the sentences (default 0.05).
top: number of levels named in the findings (default 3).
...: unused.
style: "text" (default) or "markdown".
width: line width of the text (default getOption("width")).

Value

An object of class "vlm_interpretation": a list with analysis, title, headline, findings, recommendations, cautions, facts (the numbers behind the sentences), guide (how to read the method) and sources. format() gives it as text or Markdown.

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

fm <-
  vlm_fit(
      fixed    = Pcv ~ Drug + dose + Drug:dose
    , data     = ex125
    , random   = ~ Region + Region:Drug
    , repeated = NULL
    , method   = "REML"
    , ddfm     = "satterth"
    , type     = 3
    , class    = NULL
    , bound    = TRUE
    , control  = list()
    )
vlm_interpret(
    x     = fm
  , alpha = 0.05
  , top   = 3
  )
#> Linear mixed model
#> 
#> Pcv ~ Drug + dose + Drug:dose, fitted by REML with 3 covariance parameters.
#> 
#> Findings:
#> - Of the total variance 7.63, 67.5% is due to Region, 5.1% is due to
#>   Region:Drug, 27.5% is due to the residual (within) variation.
#> - Significant at the 0.05 level: Drug (F = 64.6 on 1 and 5 df, p = 0.00048);
#>   dose (F = 21.8 on 1 and 10 df, p = 0.00089); Drug:dose (F = 7.25 on 1 and
#>   10 df, p = 0.023).
#> 
#> Recommendations:
#> - The interaction Drug:dose is significant: compare the levels of one factor
#>   within each level of the other (vlm_lsmeans() of the interaction, or
#>   vlm_estimate()) rather than main-effect means.
#> - Report means with standard errors in the broad inference space
#>   (vlm_lsmeans()); use vlm_estimate() with `random` for the narrow space of
#>   the observed levels.
#> 
#> Sources:
#> - Duchateau, L., Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models.
#>   An Introduction with Applications in Veterinary Research. International
#>   Livestock Research Institute, Nairobi. ISBN 92-9146-038-9.
#> - Patterson, H. D. and Thompson, R. (1971). Recovery of inter-block
#>   information when block sizes are unequal. Biometrika, 58(3), 545-554.
#>   doi:10.1093/biomet/58.3.545
#> - Gilmour, A. R., Thompson, R. and Cullis, B. R. (1995). Average information
#>   REML: an efficient algorithm for variance parameter estimation in linear
#>   mixed models. Biometrics, 51(4), 1440-1450. doi:10.2307/2533274
#> - Littell, R. C., Milliken, G. A., Stroup, W. W. and Wolfinger, R. D. (1996).
#>   SAS System for Mixed Models. SAS Institute, Cary, NC.
#> - Satterthwaite, F. E. (1946). An approximate distribution of estimates of
#>   variance components. Biometrics Bulletin, 2(6), 110-114.
#>   doi:10.2307/3002019
#> - Giesbrecht, F. G. and Burns, J. C. (1985). Two-stage analysis based on a
#>   mixed model: large-sample asymptotic theory and small-sample simulation
#>   results. Biometrics, 41(2), 477-486. doi:10.2307/2530872
#> - Fai, A. H.-T. and Cornelius, P. L. (1996). Approximate F-tests of multiple
#>   degree of freedom hypotheses in generalized least squares analyses of
#>   unbalanced split-plot experiments. Journal of Statistical Computation and
#>   Simulation, 54(4), 363-378. doi:10.1080/00949659608811740

Usage

Arguments

Value

See also

Author

Examples